Abstract:
Recent advances in geometry processing have resulted in a wide range of acquisition and modeling tools.
Easy accessibility of such tools have lead to large online repositories for 3D polygonal models of shapes, digitally acquired from physical objects or modeled from scratch.
However, such polygonal representations do not make specific the characteristics and invariants of the underlying shapes.
In this talk, I will describe shape analysis techniques, specially for detecting object symmetry, i.e., invariance under the action of certain transformations.
Such self-similarity is often related to form, function, utility and aesthetics.
As we enter the age of easily accessible 3D geometry, analyzing their global properties and invariants becomes important.
I will describe a simple and powerful algorithm for detecting partial and approximate symmetries in 3D shapes, and its generalization to detect regularity among the extracted symmetry transformations.
In the later part of the talk, I will describe how such high-level shape invariants, extracted in the analysis phase, can be readily used in a range of applications including shape completion,
smart geometry editing, motion visualization, shape abstraction, which are otherwise challenging to perform.